Nguồn ngoài

Demystifying AI: A Practical Introduction for Instructors

Social Sciences

Tác giả: Pamela L. Huntington
Nhà xuất bản: Merced College
Nguồn: LibreTexts
Giấy phép: CC BY
Ngôn ngữ: EN
Danh mục: Khoa học cơ bản

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Mục lục· 96 mục

01: An Introduction to AI
1.01: What is a language model
1.1.01: How language models generate text by predicting the next word
1.1.02: Examples of large language models used in education (e.g. ChatGPT Bard)
1.1.03: How Instructors and Students Are Using Large Language Models in the Classroom
1.02: What does AI learn from (Basics of training data)
1.2.01: Overview of training data sources (books websites code etc.)
1.2.02: The role of data quality and quantity in shaping AI behavior
1.2.03: Limitations- bias outdated info and misinformation in training sets
1.03: The role of algorithms in prediction and content generation
1.3.01: What an algorithm is and how it powers AI decision-making
1.3.02: How AI predicts text images and more based on patterns
1.3.03: Key difference between rules (algorithms) and examples (training data)
1.04: Examples of input-output prompts (e.g. ChatGPT DALLE)
1.4.01: What prompts are and how wording affects output
1.4.02: Sample prompts for ChatGPT and DALLE across disciplines
1.4.03: Introduction to prompt refinement (aka prompt engineering)
1.05: Where AI gets it wrong- hallucinations bias outdated info
1.5.01: What hallucinations are and why AI makes confident errors
1.5.02: Common sources of bias and misinformation in outputs
1.5.03: Why educators should always verify AI-generated content
1.06: Why AI doesnt think or understand the way humans do
1.6.01: How AI predicts rather than understands or reasons
1.6.02: Differences between AI-generated language and human cognition
1.6.03: Implications for teaching- critical AI literacy for instructors and students
02: Practical AI Applications in Teaching and Learning
2.01: AI for Course Planning
2.1.01: Drafting syllabi learning outcomes and course descriptions
2.1.02: Generating module overviews or unit plans
2.1.03: Sequencing learning activities with AI assistance
2.02: AI for Assignment and Activity Design
2.2.01: Designing case studies or scenarios tailored to your course
2.2.02: Using AI to generate examples metaphors or analogies
2.03: AI for Feedback and Assessment
2.3.01: Drafting rubric language
2.3.02: Using AI to provide formative feedback (with caveats)
2.3.03: Reviewing student reflections or written responses for patterns
2.04: AI for Student Support
2.4.01: Suggesting writing starters or research questions
2.4.02: Summarizing complex readings or concepts
2.4.03: Translating or adapting content for multilingual learners
2.05: AI for Instructor Productivity
2.5.01: Composing announcements assignment descriptions and reminders
2.5.02: Drafting professional emails or recommendation letters
2.5.03: Organizing meeting notes or to-do lists
2.06: Discipline-Neutral Examples
2.6.01: For STEM humanities social sciences CTE and the arts
2.6.02: Prompts you can adapt directly for Canvas discussions or assignments
2.07: What to Watch Out For
2.7.01: When AI is helpfuland when it might backfire
2.7.02: Importance of verifying AI-generated content
2.7.03: Transparency and ethics in using AI for instruction
03: Teaching with AI- Boundaries Bias and Best Practices
3.01: AI and Academic Integrity
3.1.01: What counts as cheating with AI
3.1.02: Should instructors disclose their own AI use
3.1.03: Encouraging transparency in student assignments
3.02: Bias and Limitations in AI
3.2.01: How AI reflects human biases in its training data
3.2.02: Risks of inaccurate or misleading information
3.2.03: Equity concerns for marginalized student groups
3.03: Student Privacy and Data Security
3.3.01: Equity concerns for marginalized student groups
3.3.02: FERPA considerations
3.3.03: Platform privacy policies (e.g. ChatGPT Grammarly Copilot)
3.04: Transparency and Consent
3.4.01: Notifying students when AI is used in course design or grading
3.4.02: Inviting students to opt in out of AI-based tools or supports
3.05: AI and Accessibility
3.5.01: How AI can support UDL and learners with disabilities
3.5.02: Cautions about assuming AI is accessible by default
3.06: Frameworks and Guidelines
3.6.01: Sample institutional or course-level policies
3.6.02: Short AI use declarations instructors can adopt
04: Moving Forward with AI in Your Teaching
4.01: Recap- What You Now Know About AI
4.1.01: Revisit the basics practical uses and ethical considerations
4.1.02: Acknowledge whats still evolving
4.02: Your AI Teaching Toolkit
4.2.01: A checklist of low-barrier tools or practices to try
4.2.02: Questions to ask when evaluating new AI tools
4.03: Reflection Prompts
4.3.01: How might AI help you solve a persistent teaching challenge
4.3.02: What concerns or hopes do you have about student use of AI
4.3.03: Where do you feel most confidentand least confidentabout using AI
4.04: Start Small- Low-Risk Ways to Try AI
4.4.01: Drafting assignment descriptions
4.4.02: Using AI to brainstorm quiz questions
4.4.03: Co-writing announcement templates
4.05: Ongoing Learning
4.5.01: 5.5.1-
4.5.02: Suggested blogs videos podcasts or newsletters
4.5.03: Staying up-to-date with tools and policy changes
4.06: Final Encouragement
4.6.01: Theres no one right way to use AI.
4.6.02: Curiosity and critical thinking are your most valuable tools.